Radio resource management and scheduling for machine type communications

Abstract

In recent years, the rapid growth of Machine Type Communications (MTC) within the Long Term Evolution (LTE) networks has introduced significant challenges in managing radio resources efficiently. MTC, characterized by massive device connectivity and diverse Quality of Service (QoS) requirements, competes with Human Type Communications (HTC), leading to high collision rates, increased delays, and sub-optimal resource utilization. Conventional LTE mechanisms, such as Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA), Slotted Aloha, and Proportional Fairness (PF), are limited in their ability to manage contention, allocate bandwidth dynamically, and adaptively address the QoS requirements of heterogeneous MTC applications. This thesis addresses these challenges by proposing and evaluating three novel mechanisms, namely: Hybrid Access Control (HAC), Dynamic Priority-based Bandwidth Allocation (DPBA), and Adaptive QoS Differentiation (AdaQoS). HAC integrates Dynamic Resource Allocation, Slotted Aloha, and CSMA/CA, achieving an 86% reduction in collision rates, a 91% decrease in access delays, and an 82% improvement in energy efficiency compared to conventional access control methods such as Slotted Aloha and CSMA/CA. DPBA introduces a dynamic bandwidth allocation scheme that prioritizes devices based on their specific requirements, resulting in a 25% improvement in throughput, a 30% reduction in latency, a 43% improvement in fairness, and 50% reduction in packet loss rate compared to existing bandwidth allocation strategies like PF, Machine Learning based Scheme (MLS) and Static Priority Scheduling (SPS). AdaQoS dynamically adjusts resource allocation to meet the specific QoS needs of MTC applications, achieving a 55% increase in scalability, a 35% improvement in fairness, a 24% boost in security, and a 54% improvement in Energy Efficiency (EE), surpassing existing machine learning-based QoS differentiation schemes. By reducing contention, optimizing bandwidth allocation dynamically, and adaptively catering to diverse QoS requirements, these mechanisms collectively enhance the efficiency, reliability, and scalability of MTC communications within LTE and beyond networks. The findings highlight their potential to optimize the coexistence of MTC and HTC, paving the way for robust and future-ready wireless communication infrastructures.